检测变压器(DETR)依赖于一对一的标签分配,即仅分配一个地面真相(GT)对象作为一个阳性对象查询,用于端到端对象检测,并且缺乏利用多个积极查询的能力。我们提出了一种新颖的DETR训练方法,称为{\ em grout detr},以支持多个积极查询。具体来说,我们将阳性分解为多个独立组,并在每个组中只保留一个阳性对象。我们在培训期间进行了简单的修改:(i)采用$ k $ of Absock Queries; (ii)对具有相同参数的每组对象查询进行解码器自我注意; (iii)为每个组执行一对一的标签分配,从而为每个GT对象提供$ K $阳性对象查询。在推论中,我们只使用一组对象查询,对架构和过程没有任何修改。我们验证了提出的方法对DITR变体的有效性,包括条件DITR,DAB-DER,DN-DEN和DINO。
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We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement ($+1.9$AP) under the same setting and achieves the best result (AP $43.4$ and $48.6$ with $12$ and $50$ epochs of training respectively) among DETR-like methods with ResNet-$50$ backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with $50\%$ training epochs. Code is available at \url{https://github.com/FengLi-ust/DN-DETR}.
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我们将Dino(\ textbf {d} etr与\ textbf {i} mpred de \ textbf {n} oising hand \ textbf {o} r boxes),一种最先进的端到端对象检测器。 % 在本文中。 Dino通过使用一种对比度方法来降级训练,一种用于锚定初始化的混合查询选择方法以及对盒子预测的两次方案,通过使用对比的方式来改善性能和效率的模型。 Dino在$ 12 $时代获得$ 49.4 $ ap,$ 12.3 $ ap in Coco $ 24 $时期,带有Resnet-50骨干和多尺度功能,可显着改善$ \ textbf {+6.0} $ \ textbf {ap}和ap {ap}和ap}和$ \ textbf {+2.7} $ \ textbf {ap}与以前的最佳detr样模型相比,分别是dn-detr。 Dino在模型大小和数据大小方面都很好地缩放。没有铃铛和哨子,在对objects365数据集进行了swinl骨架的预训练后,Dino在两个Coco \ texttt {val2017}($ \ textbf {63.2} $ \ textbf {ap ap})和\ testtt { -dev}(\ textbf {$ \ textbf {63.3} $ ap})。与排行榜上的其他模型相比,Dino大大降低了其模型大小和预训练数据大小,同时实现了更好的结果。我们的代码将在\ url {https://github.com/ideacvr/dino}提供。
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Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture. Code is available at https://github.com/jozhang97/DETA.
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一对一的匹配是DETR建立其端到端功能的关键设计,因此对象检测不需要手工制作的NMS(非最大抑制)方法来删除重复检测。这种端到端的签名对于DETR的多功能性很重要,并且已将其推广到广泛的视觉问题,包括实例/语义分割,人体姿势估计以及基于点云/多视图的检测,但是,我们注意到,由于分配为正样本的查询太少,因此一对一的匹配显着降低了阳性样品的训练效率。本文提出了一种基于混合匹配方案的简单而有效的方法,该方法将原始的一对一匹配分支与辅助查询结合在一起,这些查询在训练过程中使用一对一的匹配损失。该混合策略已被证明可显着提高训练效率并提高准确性。在推断中,仅使用原始的一对一匹配分支,从而维持端到端的优点和相同的DETR推断效率。该方法命名为$ \ MATHCAL {H} $ - DETR,它表明可以在各种视觉任务中始终如一地改进各种代表性的DITR方法,包括可变形,3DDER/PETRV2,PETR和TRANDRACK, ,其他。代码将在以下网址提供:https://github.com/hdetr
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This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4-2.8 AP improvement.
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DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10× less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is released at https:// github.com/fundamentalvision/Deformable-DETR.
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DETR方法中引入的查询机制正在改变对象检测的范例,最近有许多基于查询的方法获得了强对象检测性能。但是,当前基于查询的检测管道遇到了以下两个问题。首先,需要多阶段解码器来优化随机初始化的对象查询,从而产生较大的计算负担。其次,训练后的查询是固定的,导致不满意的概括能力。为了纠正上述问题,我们在较快的R-CNN框架中提出了通过查询生成网络预测的特征对象查询,并开发了一个功能性的查询R-CNN。可可数据集的广泛实验表明,我们的特征查询R-CNN获得了所有R-CNN探测器的最佳速度准确性权衡,包括最近的最新稀疏R-CNN检测器。该代码可在\ url {https://github.com/hustvl/featurized-queryrcnn}中获得。
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在本文中,我们对检测变压器(DETR)感兴趣,这是一种基于变压器编码器编码器架构的端到端对象检测方法,而无需手工制作的后处理,例如NMS。受到有条件的Detr的启发,这是一种具有快速训练收敛性的改进的DETR,对内部解码器层提出了盒子查询(最初称为空间查询),我们将对象查询重新将对象查询重新布置为盒子查询的格式,该格式是参考参考嵌入的组成点和框相对于参考点的转换。该重新制定表明在更快地使用R-CNN中广泛研究的DETR中的对象查询与锚固框之间的联系。此外,我们从图像内容中学习了盒子查询,从而进一步提高了通过快速训练收敛的有条件DETR的检测质量。此外,我们采用轴向自我注意的想法来节省内存成本并加速编码器。所得的检测器(称为条件DETR V2)取得比条件DETR更好的结果,可节省内存成本并更有效地运行。例如,对于DC $ 5 $ -Resnet- $ 50 $骨干,我们的方法在可可$ Val $ set上获得了$ 44.8 $ ap,$ 16.4 $ fps和有条件的detr相比,它运行了$ 1.6 \ tims $ $ $ $ $,节省$ 74 $ \ \ \ \ \ \ \ \ \ \ \ \ \ $ 74美元总体内存成本的百分比,并提高$ 1.0 $ ap得分。
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在本文中,我们提出了一种用于基于变压器的对象检测的新型查询设计。在以前的基于变压器的检测器中,对象查询是一组学习的嵌入。但是,每个学习的嵌入都没有明确的物理意义,我们无法解释它将在哪里关注。由于每个对象查询的预测时隙没有特定模式,难以优化。换句话说,每个对象查询不会专注于特定区域。为了解决这些问题,在我们的查询设计中,对象查询基于锚点,其广泛用于基于CNN的检测器。所以每个对象查询都侧重于锚点附近的对象。此外,我们的查询设计可以在一个位置预测多个对象来解决难度:“一个区域,多个对象”。此外,我们设计了一个注意力,可以降低内存成本,同时实现比DETR中的标准注意力相似或更好的性能。由于查询设计和注意力变化,所提出的探测器,我们称之为锚点DETR,可以实现更好的性能,并比DEDR更快地运行10美元\ Times $更少的训练时期。例如,当使用Reset50-DC5功能进行培训50时,它在MSCOCO DataSet上实现44.2 AP。对MSCOCO基准的广泛实验证明了所提出的方法的有效性。代码可用于\ url {https://github.com/megvii-research/anchordetr}。
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检测变压器已在富含样品的可可数据集上实现了竞争性能。但是,我们显示他们中的大多数人在小型数据集(例如CityScapes)上遭受了大量的性能下降。换句话说,检测变压器通常是渴望数据的。为了解决这个问题,我们通过逐步过渡从数据效率的RCNN变体到代表性的DETR,从经验中分析影响数据效率的因素。经验结果表明,来自本地图像区域的稀疏特征采样可容纳关键。基于此观察结果,我们通过简单地简单地交替如何在跨意义层构建键和价值序列,从而减少现有检测变压器的数据问题,并对原始模型进行最小的修改。此外,我们引入了一种简单而有效的标签增强方法,以提供更丰富的监督并提高数据效率。实验表明,我们的方法可以很容易地应用于不同的检测变压器,并在富含样品和样品的数据集上提高其性能。代码将在\ url {https://github.com/encounter1997/de-detrs}上公开提供。
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本文介绍了端到端的实例分段框架,称为SOIT,该段具有实例感知变压器的段对象。灵感来自Detr〜\ Cite {carion2020end},我们的方法视图实例分段为直接设置预测问题,有效地消除了对ROI裁剪,一对多标签分配等许多手工制作组件的需求,以及非最大抑制( nms)。在SOIT中,通过在全局图像上下文下直接地将多个查询直接理解语义类别,边界框位置和像素 - WISE掩码的一组对象嵌入。类和边界盒可以通过固定长度的向量轻松嵌入。尤其是由一组参数嵌入像素方面的掩模以构建轻量级实例感知变压器。之后,实例感知变压器产生全分辨率掩码,而不涉及基于ROI的任何操作。总的来说,SOIT介绍了一个简单的单级实例分段框架,它是无乐和NMS的。 MS Coco DataSet上的实验结果表明,优于最先进的实例分割显着的优势。此外,在统一查询嵌入中的多个任务的联合学习还可以大大提高检测性能。代码可用于\ url {https://github.com/yuxiaodonghri/soit}。
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DETR是使用变压器编码器 - 解码器架构的第一端到端对象检测器,并在高分辨率特征映射上展示竞争性能但低计算效率。随后的工作变形Detr,通过更换可变形的关注来提高DEDR的效率,这实现了10倍的收敛性和改进的性能。可变形DETR使用多尺度特征来改善性能,但是,与DETR相比,编码器令牌的数量增加了20倍,编码器注意的计算成本仍然是瓶颈。在我们的初步实验中,我们观察到,即使只更新了编码器令牌的一部分,检测性能也几乎没有恶化。灵感来自该观察,我们提出了稀疏的DETR,其仅选择性更新预期的解码器预期的令牌,从而有效地检测模型。此外,我们表明在编码器中的所选令牌上应用辅助检测丢失可以提高性能,同时最小化计算开销。即使在Coco数据集上只有10%的编码器令牌,我们验证稀疏DETR也可以比可变形DETR实现更好的性能。尽管只有编码器令牌稀疏,但总计算成本减少了38%,与可变形的Detr相比,每秒帧(FPS)增加42%。代码可在https://github.com/kakaobrain/sparse-dett
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Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerate Transformer detector convergency. These methods gradually refine the reference points to the center of target objects and imbue object queries with the updated central reference information for spatially conditional attention. However, centralizing reference points may severely deteriorate queries' saliency and confuse detectors due to the indiscriminative spatial prior. To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects. In SAP-DETR, we explicitly initialize a query-specific reference point for each object query, gradually aggregate them into an instance object, and then predict the distance from each side of the bounding box to these points. By rapidly attending to query-specific reference region and other conditional extreme regions from the image features, SAP-DETR can effectively bridge the gap between the salient point and the query-based Transformer detector with a significant convergency speed. Our extensive experiments have demonstrated that SAP-DETR achieves 1.4 times convergency speed with competitive performance. Under the standard training scheme, SAP-DETR stably promotes the SOTA approaches by 1.0 AP. Based on ResNet-DC-101, SAP-DETR achieves 46.9 AP.
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The DETR object detection approach applies the transformer encoder and decoder architecture to detect objects and achieves promising performance. In this paper, we present a simple approach to address the main problem of DETR, the slow convergence, by using representation learning technique. In this approach, we detect an object bounding box as a pair of keypoints, the top-left corner and the center, using two decoders. By detecting objects as paired keypoints, the model builds up a joint classification and pair association on the output queries from two decoders. For the pair association we propose utilizing contrastive self-supervised learning algorithm without requiring specialized architecture. Experimental results on MS COCO dataset show that Pair DETR can converge at least 10x faster than original DETR and 1.5x faster than Conditional DETR during training, while having consistently higher Average Precision scores.
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In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at \url{https://github.com/IDEACVR/MaskDINO}.
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最近提出的检测变压器(DETR)已建立了一个完全端到端的范式以进行对象检测。但是,DETR遭受慢训练的融合,这阻碍了其对各种检测任务的适用性。我们观察到,由于对象查询和编码图像特征之间的语义不一致,DETR的缓慢收敛在很大程度上归因于将对象查询与相关区域匹配的困难。通过此观察,我们设计了与DETR ++(SAM-DETR ++)设计的语义对齐匹配,以加速DETR的收敛并改善检测性能。 SAM-DETR ++的核心是一个插件模块,该模块将对象查询和编码图像功能投射到相同的功能嵌入空间中,在该空间中,每个对象查询都可以轻松地与具有相似语义的相关区域匹配。此外,SAM-DETR ++搜索了多个代表性关键点,并利用其功能以具有增强的表示能力的语义对齐匹配。此外,SAM-DETR ++可以根据设计的语义对准匹配,以粗到5的方式有效地融合多尺度特征。广泛的实验表明,所提出的SAM-DETR ++实现了优越的收敛速度和竞争性检测准确性。此外,作为一种插件方法,SAM-DETR ++可以以更好的性能补充现有的DITR收敛解决方案,仅使用12个训练时代获得44.8%的AP和49.1%的AP,并使用Resnet-50上的CoCo Val2017上的50个训练时代获得50个训练时期。代码可在https://github.com/zhanggongjie/sam-detr上找到。
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虽然用变压器(DETR)的检测越来越受欢迎,但其全球注意力建模需要极其长的培训期,以优化和实现有前途的检测性能。现有研究的替代方案主要开发先进的特征或嵌入设计来解决培训问题,指出,基于地区的兴趣区域(ROI)的检测细化可以很容易地帮助减轻DETR方法培训的难度。基于此,我们在本文中介绍了一种新型的经常性闪闪发光的解码器(Rego)。特别是,REGO采用多级复发处理结构,以帮助更准确地逐渐关注前景物体。在每个处理阶段,从ROI的闪烁特征提取视觉特征,其中来自上阶段的检测结果的放大边界框区域。然后,引入了基于一瞥的解码器,以提供基于前一级的瞥见特征和注意力建模输出的精细检测结果。在实践中,Refo可以很容易地嵌入代表性的DETR变体,同时保持其完全端到端的训练和推理管道。特别地,Refo帮助可变形的DETR在MSCOCO数据集上实现44.8AP,只有36个训练时期,与需要500和50时期的第一DETR和可变形的DETR相比,分别可以分别实现相当的性能。实验还表明,Rego始终如一地提升不同DETR探测器的性能高达7%的相对增益,在相同的50次训练时期。代码可通过https://github.com/zhechen/deformable-detr-rego获得。
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Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this paper proposes a Task-Aligned Detection Transformer network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine Transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the Query Selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a Task-Aligned Dual-Head block to decouple the classification and regression tasks. Based on our self-developed PMMW security screening dataset, experimental results including comparison with State-Of-The-Art (SOTA) methods and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality.
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视觉变压器(VIT)正在改变对象检测方法的景观。 VIT的自然使用方法是用基于变压器的骨干替换基于CNN的骨干,该主链很简单有效,其价格为推理带来了可观的计算负担。更微妙的用法是DEDR家族,它消除了对物体检测中许多手工设计的组件的需求,但引入了一个解码器,要求超长时间进行融合。结果,基于变压器的对象检测不能在大规模应用中占上风。为了克服这些问题,我们提出了一种新型的无解码器基于完全变压器(DFFT)对象检测器,这是第一次在训练和推理阶段达到高效率。我们通过居中两个切入点来简化反对检测到仅编码单级锚点的密集预测问题:1)消除训练感知的解码器,并利用两个强的编码器来保留单层特征映射预测的准确性; 2)探索具有有限的计算资源的检测任务的低级语义特征。特别是,我们设计了一种新型的轻巧的面向检测的变压器主链,该主链有效地捕获了基于良好的消融研究的丰富语义的低级特征。 MS Coco基准测试的广泛实验表明,DFFT_SMALL的表现优于2.5%AP,计算成本降低28%,$ 10 \ $ 10 \乘以$ 10 \乘以$较少的培训时期。与尖端的基于锚的探测器视网膜相比,DFFT_SMALL获得了超过5.5%的AP增益,同时降低了70%的计算成本。
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